B-ACT improves label efficiency in temporal action segmentation by selecting only boundary frames for annotation via a two-stage uncertainty-driven process that fuses neighborhood uncertainty, class ambiguity, and temporal dynamics.
Fact: Frame-action cross-attention temporal modeling for efficient action segmentation
2 Pith papers cite this work. Polarity classification is still indexing.
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SASI combines skeleton-based graph convolutions with sub-action semantics for improved early action recognition on the BABEL dataset.
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Boundary-Centric Active Learning for Temporal Action Segmentation
B-ACT improves label efficiency in temporal action segmentation by selecting only boundary frames for annotation via a two-stage uncertainty-driven process that fuses neighborhood uncertainty, class ambiguity, and temporal dynamics.
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SASI: Leveraging Sub-Action Semantics for Robust Early Action Recognition in Human-Robot Interaction
SASI combines skeleton-based graph convolutions with sub-action semantics for improved early action recognition on the BABEL dataset.